Data-driven approaches to extracting informational and linguistic value from large online communities

Social media platforms host large, persistent communities that produce vast volumes of human-authored text with clear value for research and practice. For AI systems based on large language models (LLMs), such corpora are critical for model training – but there is limited prior work on their benefits. This dissertation examines large-scale online communities along these two complementary dimensions: informational value (what information and knowledge can be extracted) and linguistic value (how community-specific language can be modelled and evaluated). From a perspective of computational linguistics and social computing, this work combines data-mining approaches with deep-learning techniques and intersects the domains of finance, history, and creative writing. The communities are chosen for distinctive properties, including previously unstudied cases. Informational value is extracted from some of the largest fact-driven communities: Popular Reddit finance communities are examined for evidence of collective intelligence, discussion-based trading signals are identified, and investment strategy performance is assessed in historical simulations against standard benchmarks. Leveraging the carefully curated r/AskHistorians community, LLMs’ abilities for factual acquisition are evaluated. Results show that while data-mining pipelines extract signals successfully within a pre-defined scope and hint at collective intelligence within the reviewed communities, LLMs underperform at memorising domain-specific facts even after targeted fine-tuning. Regarding linguistic value, creative and factual communities are studied: LLMs learn and reproduce community-specific linguistic characteristics in creative shortform generation and finance-related long-form question answering, improving creativity and wit (from creative posts) and simplicity and specificity (from factual discussions), demonstrating the linguistic value of high-quality community datasets. Overall, the dissertation shows when and how large communities yield valuable information and linguistic patterns. It contributes novel insights about diverse, relevant communities, their value, and the opportunities and limits of extracting it. Methods and findings are applicable to future community-centric AI research and evaluation and practice. In addition, papers, datasets, and code have been released.